Deepfake Detection for Video Conferencing: Zoom, Teams, Meet, and Webex
Verifying identities during workplace video meetings is becoming increasingly important, especially since deepfake technology can create realistic changes to faces, voices, or identities. Deepfake detection for video conferencing can help the workforce verify these identities during live audio and video calls on platforms such as Zoom, Teams, Google Meet, and Webex.
Diopter is one such tool that continuously scores each participant for signs of synthetic or manipulated video throughout the call. It identifies potential manipulation to provide a confidence-based verdict.
Key Takeaways
- AI-generated faces and voices can make attackers appear like executives, employees or business partners during live video calls.
- Deepfake attacks can target human trust without breaching networks or triggering standard security alerts.
- Unexpected requests that involve payments, sensitive information, or access must be confirmed through another trusted channel.
- Employee training, authentication controls, limited information sharing, and real-time detection work together to reduce deepfake-related risks.
- Diopter scores video continuously throughout the call for signs of AI generation or manipulation, including face swaps, voice cloning and other forms of synthetic media.
How are deepfakes being used in video meetings?
Deepfake technology can be misused to impersonate executives, employees, clients, or business partners during virtual meetings. An attacker may appear on screen as someone trusted and use a cloned voice to make the impersonation more convincing.
Such impersonation can be used in deepfake video call scams to give false instructions, influence important decisions, or gain access to confidential discussions.
According to the Federal Bureau of Investigation, in 2025 there were reports of losses worth $893 million across 22,364 complaints involving AI-related fraud.
There is a pressing need to reliably verify identities. Deepfake detection tools help organizations identify signs of manipulated audio or video and can add a layer of verification for online meetings.
Real-life example: the Arup deepfake video call
In January 2024, a finance employee at global engineering firm Arup authorized 15 separate transactions amounting to HK$200 million shortly after joining a video call. The other participants appeared to be the company’s CFO and other senior executives, but they were AI-generated impersonations using synthetic video and audio.
This deepfake video call scam showed that traditional cybersecurity controls cannot always prevent attacks, and that deepfake attacks can exploit human trust without breaching a company’s network.
Here is what went wrong, and the lessons:
- No network breach: Attackers did not steal credentials or enter Arup’s systems.
- AI impersonation: Deepfake faces and cloned voices made the fake meeting appear genuine.
- Trust was exploited: Familiar faces and voices created confidence in the attackers’ instructions.
- Controls were bypassed: Standard security measures could not verify whether meeting participants were real.
- Independent verification matters: Out-of-band identity and payment verification is an important step.
- Deepfake detection can help: Detection tools can identify potential synthetic or manipulated video and audio during live interactions.
- Human awareness helps: Urgent, confidential financial requests should always have additional verification.
Why is deepfake detection for video conferencing essential?
A successful deepfake impersonation can expose organizations to severe financial, operational and reputational risks. Deepfake detection for video conferencing helps organizations verify identities and identify signs of manipulated audio or video before they lead to costly mistakes.
The most prominent business impacts include:
Financial fraud
Impersonators may pose as senior executives and request payments, fund transfers, or changes to financial details.
Unauthorized access
Fake identities can be used to gain access to confidential meetings, systems, documents, or sensitive business information.
Loss of trust
A deepfake incident can affect trust and confidence among employees, customers, and business partners, potentially damaging an organization’s reputation.
Operational disruption
Teams may need to investigate incidents, reverse fraudulent actions, and strengthen security measures, increasing costs and downtime.
What does a deepfake video call attack look like?
A deepfake video call scam follows a sequence to make an artificial identity appear trustworthy. Diopter describes this as a pattern of authority, urgency, isolation, escalation, and the final ask, and continuously analyzes the call for video, audio, and behavioral signals.
| Stage | What happens |
|---|---|
| 1. Impersonation | The attacker adopts the identity of an executive, colleague, vendor, or other trusted person. |
| 2. Meeting joined | The attacker enters a legitimate video call using the fake identity. |
| 3. Video and audio manipulation | AI-generated faces, face swaps, or cloned voices make the person look and sound genuine. Face swap techniques can replace a participant’s face with that of a trusted executive, and voice cloning can replicate how that person sounds. |
| 4. Trust building | The target is encouraged to accept the identity because of a familiar appearance, voice, authority, and conversation style. |
| 5. Urgency and isolation | Attackers create pressure with urgent tasks and isolation through confidential conversations, leaving little time for independent verification. |
| 6. Escalation | Small requests gradually lead to access, approval, or financial instructions. |
| 7. Final action | The target approves a payment, shares information, or grants access based on a false identity. |
Also read: Deepfake Video Detection Explained
How does Diopter detect deepfakes during live video calls?
Diopter analyzes the video continuously during a live call rather than relying on a single frame or a one-time identity check. This continuous approach helps with face swap detection throughout the meeting. It detects and scores each visible face for signs of AI generation or manipulation, and keeps evaluating participants who join late or turn their cameras on later.
These individual results are then combined into an overall verdict, with confidence levels and supporting evidence. Diopter can also consider signals such as liveness and the wider conversation, detecting pressure or urgent requests. Teams can then decide when additional verification is needed.
The platform’s live deepfake video detection process is as follows:
- Live video call begins.
- Video stream is continuously sampled.
- Each visible face is detected and analyzed, continuing throughout the call.
- AI-generated or manipulated video signals are scored.
- Liveness and other call signals are assessed.
- Individual face results are combined.
- An overall verdict is reached: AI, Mixed, Clean, or Inconclusive.
- The team can verify the participant before taking an important action.
This approach gives security and fraud teams a continuous view of the call instead of treating the opening frame as proof of identity.
Deepfake detection across Zoom, Teams, Meet, and Webex
Deepfake video call scams are common on conferencing platforms such as Zoom, Microsoft Teams, Google Meet, and Webex. Diopter works alongside the video and voice tools your team already uses. There is no need to upload calls separately for analysis or change how your team joins meetings.
Across Zoom, Teams, Meet, and Webex, Diopter continuously analyzes the video during the live meeting. Participants can be assessed throughout the call, even when someone joins late or turns on their camera partway through.
Diopter can be rolled out through your existing mobile device management (MDM) tools, such as Microsoft Intune or Jamf. There is no caller-side installation, so employees can continue using their usual video and voice tools while Diopter runs alongside them.
Why single-frame deepfake detection can miss live attacks
Single-frame deepfake detection analyzes an individual frame from a video call to determine whether it is AI generated or manipulated. It provides only a snapshot of the participant and may miss signs that become apparent during the call.
A deepfake may appear convincing in one frame. Signs of manipulation can appear in changes to facial movements, lighting, head position, compression, and other video details over time. This creates a limitation for attacks where the synthetic identity appears later, or becomes more convincing at the moment the attacker makes an important request.
Diopter uses a different strategy for deepfake detection in video conferencing:
- Continuous video scoring: Video is scored throughout the call, not only at the beginning.
- Face-level scoring: Each detected face is analyzed and scored for signs of generation or manipulation.
- Ongoing assessment: Scoring continues when participants join late or turn on their cameras midway.
- Critical-moment coverage: Participants who appear only during an important request can still be assessed.
- Overall scoring: Per-face results are combined into an overall verdict with a confidence score and supporting evidence.
How do you prevent deepfake attacks during video meetings?
Preventing deepfake video call scams requires strong verification steps at several levels. Since fake faces and voices can appear convincing, organizations should not rely only on what they see or hear.
- Verify unexpected requests: Confirm payment instructions, access requests, or password changes using a trusted, separate channel or contact.
- Use stronger authentication: Require multi-factor authentication for higher-risk actions such as financial transactions, account changes, or access to sensitive systems.
- Limit sensitive information: Do not share confidential business information or approve important transactions based only on instructions given during a video call.
- Train employees: Teach teams to identify unusual requests, urgency, pressure, and attempts to bypass normal processes. Give employees a clear way to pause and escalate concerns.
- Use live detection: For high-risk meetings, use deepfake detection tools that analyze video, identity, and conversation signals during the call.
How do detection, authentication and training work together?
Organizations can reduce the risk of deepfake video call attacks by combining real-time detection with independent verification, stronger authentication, and employee awareness. Traditional security measures remain important, but they may not be enough when an attacker uses a synthetic face or voice to influence decisions.
Deepfake detection for video conferencing adds another layer of protection. It analyzes participants during live calls and identifies signs of manipulation. Real-time detection, stronger authentication, and employee training together make video meetings more trustworthy.
Frequently Asked Questions
What should organizations look for in a face swap detection tool?
Organizations should look for a tool that analyzes video continuously, offers face-level analysis, can assess participants who join or turn on their cameras later, and returns confidence-based results with clear supporting evidence for each verdict. The tool should also fit into existing meeting and security workflows.
How can enterprises combine synthetic media detection with existing security controls?
Synthetic media detection works best as an additional layer within an organization’s existing security controls. Enterprises can combine real-time detection with multi-factor authentication, out-of-band verification, access controls, and employee training.
Can Diopter detect deepfakes in real time without interrupting a meeting?
Diopter is designed to analyze live video and audio signals during a call without requiring participants to stop or restart the meeting. This allows potential deepfake activity to be assessed in real time.
What happens when Diopter detects a potential deepfake during a live call?
When Diopter identifies signals that may indicate a deepfake, it flags the activity for further attention so security teams or meeting participants can take appropriate verification steps. Every verdict includes a confidence level rather than a simple yes or no. If a call or file cannot be fully analyzed, Diopter reports that rather than marking it as clean.
Can Diopter integrate with an organization’s existing video-conferencing and security setup?
Diopter can be integrated with existing video-conferencing and security systems, though the specifics depend on the organization’s technical setup and integration requirements.
Know who is really on the call
Diopter scores every face on the call as it happens, so a familiar voice cannot approve a payment on its own.
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